Overview

Dataset statistics

Number of variables13
Number of observations374
Missing cells219
Missing cells (%)4.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.1 KiB
Average record size in memory104.4 B

Variable types

Numeric8
Categorical5

Alerts

Sleep Disorder has 219 (58.6%) missing valuesMissing
Person ID is uniformly distributedUniform
Person ID has unique valuesUnique

Reproduction

Analysis started2024-05-01 19:18:40.092975
Analysis finished2024-05-01 19:18:43.466790
Duration3.37 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Person ID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct374
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.5
Minimum1
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-01T20:18:43.513771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.65
Q194.25
median187.5
Q3280.75
95-th percentile355.35
Maximum374
Range373
Interquartile range (IQR)186.5

Descriptive statistics

Standard deviation108.10874
Coefficient of variation (CV)0.57657995
Kurtosis-1.2
Mean187.5
Median Absolute Deviation (MAD)93.5
Skewness0
Sum70125
Variance11687.5
MonotonicityStrictly increasing
2024-05-01T20:18:43.577429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
247 1
 
0.3%
256 1
 
0.3%
255 1
 
0.3%
254 1
 
0.3%
253 1
 
0.3%
252 1
 
0.3%
251 1
 
0.3%
250 1
 
0.3%
249 1
 
0.3%
Other values (364) 364
97.3%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
374 1
0.3%
373 1
0.3%
372 1
0.3%
371 1
0.3%
370 1
0.3%
369 1
0.3%
368 1
0.3%
367 1
0.3%
366 1
0.3%
365 1
0.3%

Gender
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Male
189 
Female
185 

Length

Max length6
Median length4
Mean length4.9893048
Min length4

Characters and Unicode

Total characters1866
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 189
50.5%
Female 185
49.5%

Length

2024-05-01T20:18:43.642984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T20:18:43.698184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
male 189
50.5%
female 185
49.5%

Most occurring characters

ValueCountFrequency (%)
e 559
30.0%
a 374
20.0%
l 374
20.0%
M 189
 
10.1%
F 185
 
9.9%
m 185
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1866
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 559
30.0%
a 374
20.0%
l 374
20.0%
M 189
 
10.1%
F 185
 
9.9%
m 185
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1866
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 559
30.0%
a 374
20.0%
l 374
20.0%
M 189
 
10.1%
F 185
 
9.9%
m 185
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1866
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 559
30.0%
a 374
20.0%
l 374
20.0%
M 189
 
10.1%
F 185
 
9.9%
m 185
 
9.9%

Age
Real number (ℝ)

Distinct31
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.184492
Minimum27
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-01T20:18:43.746278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile29.65
Q135.25
median43
Q350
95-th percentile58
Maximum59
Range32
Interquartile range (IQR)14.75

Descriptive statistics

Standard deviation8.6731335
Coefficient of variation (CV)0.20560005
Kurtosis-0.90977955
Mean42.184492
Median Absolute Deviation (MAD)7
Skewness0.25722214
Sum15777
Variance75.223244
MonotonicityIncreasing
2024-05-01T20:18:43.805530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
43 34
 
9.1%
44 30
 
8.0%
37 20
 
5.3%
38 20
 
5.3%
50 20
 
5.3%
31 18
 
4.8%
32 17
 
4.5%
53 17
 
4.5%
59 16
 
4.3%
39 15
 
4.0%
Other values (21) 167
44.7%
ValueCountFrequency (%)
27 1
 
0.3%
28 5
 
1.3%
29 13
3.5%
30 13
3.5%
31 18
4.8%
32 17
4.5%
33 13
3.5%
34 2
 
0.5%
35 12
3.2%
36 12
3.2%
ValueCountFrequency (%)
59 16
4.3%
58 6
 
1.6%
57 9
2.4%
56 2
 
0.5%
55 2
 
0.5%
54 7
 
1.9%
53 17
4.5%
52 9
2.4%
51 8
 
2.1%
50 20
5.3%

Occupation
Categorical

Distinct11
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Nurse
73 
Doctor
71 
Engineer
63 
Lawyer
47 
Teacher
40 
Other values (6)
80 

Length

Max length20
Median length17
Mean length7.2994652
Min length5

Characters and Unicode

Total characters2730
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowSoftware Engineer
2nd rowDoctor
3rd rowDoctor
4th rowSales Representative
5th rowSales Representative

Common Values

ValueCountFrequency (%)
Nurse 73
19.5%
Doctor 71
19.0%
Engineer 63
16.8%
Lawyer 47
12.6%
Teacher 40
10.7%
Accountant 37
9.9%
Salesperson 32
8.6%
Software Engineer 4
 
1.1%
Scientist 4
 
1.1%
Sales Representative 2
 
0.5%

Length

2024-05-01T20:18:43.866782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nurse 73
19.2%
doctor 71
18.7%
engineer 67
17.6%
lawyer 47
12.4%
teacher 40
10.5%
accountant 37
9.7%
salesperson 32
8.4%
software 4
 
1.1%
scientist 4
 
1.1%
sales 2
 
0.5%
Other values (2) 3
 
0.8%

Most occurring characters

ValueCountFrequency (%)
e 417
15.3%
r 337
12.3%
n 247
 
9.0%
o 215
 
7.9%
c 189
 
6.9%
a 166
 
6.1%
t 161
 
5.9%
s 145
 
5.3%
u 110
 
4.0%
i 77
 
2.8%
Other values (18) 666
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 417
15.3%
r 337
12.3%
n 247
 
9.0%
o 215
 
7.9%
c 189
 
6.9%
a 166
 
6.1%
t 161
 
5.9%
s 145
 
5.3%
u 110
 
4.0%
i 77
 
2.8%
Other values (18) 666
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 417
15.3%
r 337
12.3%
n 247
 
9.0%
o 215
 
7.9%
c 189
 
6.9%
a 166
 
6.1%
t 161
 
5.9%
s 145
 
5.3%
u 110
 
4.0%
i 77
 
2.8%
Other values (18) 666
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 417
15.3%
r 337
12.3%
n 247
 
9.0%
o 215
 
7.9%
c 189
 
6.9%
a 166
 
6.1%
t 161
 
5.9%
s 145
 
5.3%
u 110
 
4.0%
i 77
 
2.8%
Other values (18) 666
24.4%

Sleep Duration
Real number (ℝ)

Distinct27
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1320856
Minimum5.8
Maximum8.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-01T20:18:43.920822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5.8
5-th percentile6
Q16.4
median7.2
Q37.8
95-th percentile8.4
Maximum8.5
Range2.7
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation0.79565673
Coefficient of variation (CV)0.11156018
Kurtosis-1.2865062
Mean7.1320856
Median Absolute Deviation (MAD)0.7
Skewness0.03755439
Sum2667.4
Variance0.63306963
MonotonicityNot monotonic
2024-05-01T20:18:43.976126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
7.2 36
 
9.6%
6 31
 
8.3%
7.8 28
 
7.5%
6.5 26
 
7.0%
6.1 25
 
6.7%
7.7 24
 
6.4%
6.6 20
 
5.3%
7.1 19
 
5.1%
8.1 15
 
4.0%
7.3 14
 
3.7%
Other values (17) 136
36.4%
ValueCountFrequency (%)
5.8 2
 
0.5%
5.9 4
 
1.1%
6 31
8.3%
6.1 25
6.7%
6.2 12
 
3.2%
6.3 13
3.5%
6.4 9
 
2.4%
6.5 26
7.0%
6.6 20
5.3%
6.7 5
 
1.3%
ValueCountFrequency (%)
8.5 13
3.5%
8.4 14
3.7%
8.3 5
 
1.3%
8.2 11
 
2.9%
8.1 15
4.0%
8 13
3.5%
7.9 7
 
1.9%
7.8 28
7.5%
7.7 24
6.4%
7.6 10
 
2.7%

Quality of Sleep
Real number (ℝ)

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3128342
Minimum4
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-01T20:18:44.025026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q16
median7
Q38
95-th percentile9
Maximum9
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1969559
Coefficient of variation (CV)0.1636788
Kurtosis-0.74827554
Mean7.3128342
Median Absolute Deviation (MAD)1
Skewness-0.20744763
Sum2735
Variance1.4327035
MonotonicityNot monotonic
2024-05-01T20:18:44.072686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 109
29.1%
6 105
28.1%
7 77
20.6%
9 71
19.0%
5 7
 
1.9%
4 5
 
1.3%
ValueCountFrequency (%)
4 5
 
1.3%
5 7
 
1.9%
6 105
28.1%
7 77
20.6%
8 109
29.1%
9 71
19.0%
ValueCountFrequency (%)
9 71
19.0%
8 109
29.1%
7 77
20.6%
6 105
28.1%
5 7
 
1.9%
4 5
 
1.3%

Physical Activity Level
Real number (ℝ)

Distinct16
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.171123
Minimum30
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-01T20:18:44.117718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30
Q145
median60
Q375
95-th percentile90
Maximum90
Range60
Interquartile range (IQR)30

Descriptive statistics

Standard deviation20.830804
Coefficient of variation (CV)0.35204341
Kurtosis-1.2660678
Mean59.171123
Median Absolute Deviation (MAD)15
Skewness0.074486903
Sum22130
Variance433.92238
MonotonicityNot monotonic
2024-05-01T20:18:44.167359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
60 70
18.7%
30 68
18.2%
45 68
18.2%
75 67
17.9%
90 67
17.9%
40 6
 
1.6%
55 6
 
1.6%
35 4
 
1.1%
50 4
 
1.1%
70 3
 
0.8%
Other values (6) 11
 
2.9%
ValueCountFrequency (%)
30 68
18.2%
32 2
 
0.5%
35 4
 
1.1%
40 6
 
1.6%
42 2
 
0.5%
45 68
18.2%
47 1
 
0.3%
50 4
 
1.1%
55 6
 
1.6%
60 70
18.7%
ValueCountFrequency (%)
90 67
17.9%
85 2
 
0.5%
80 2
 
0.5%
75 67
17.9%
70 3
 
0.8%
65 2
 
0.5%
60 70
18.7%
55 6
 
1.6%
50 4
 
1.1%
47 1
 
0.3%

Stress Level
Real number (ℝ)

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3850267
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-01T20:18:44.216363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q14
median5
Q37
95-th percentile8
Maximum8
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7745264
Coefficient of variation (CV)0.32952974
Kurtosis-1.3273066
Mean5.3850267
Median Absolute Deviation (MAD)2
Skewness0.15432958
Sum2014
Variance3.1489441
MonotonicityNot monotonic
2024-05-01T20:18:44.263616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 71
19.0%
8 70
18.7%
4 70
18.7%
5 67
17.9%
7 50
13.4%
6 46
12.3%
ValueCountFrequency (%)
3 71
19.0%
4 70
18.7%
5 67
17.9%
6 46
12.3%
7 50
13.4%
8 70
18.7%
ValueCountFrequency (%)
8 70
18.7%
7 50
13.4%
6 46
12.3%
5 67
17.9%
4 70
18.7%
3 71
19.0%

BMI Category
Categorical

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Normal
195 
Overweight
148 
Normal Weight
21 
Obese
 
10

Length

Max length13
Median length6
Mean length7.9491979
Min length5

Characters and Unicode

Total characters2973
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOverweight
2nd rowNormal
3rd rowNormal
4th rowObese
5th rowObese

Common Values

ValueCountFrequency (%)
Normal 195
52.1%
Overweight 148
39.6%
Normal Weight 21
 
5.6%
Obese 10
 
2.7%

Length

2024-05-01T20:18:44.319690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T20:18:44.369791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
normal 216
54.7%
overweight 148
37.5%
weight 21
 
5.3%
obese 10
 
2.5%

Most occurring characters

ValueCountFrequency (%)
r 364
12.2%
e 337
11.3%
N 216
 
7.3%
m 216
 
7.3%
a 216
 
7.3%
l 216
 
7.3%
o 216
 
7.3%
h 169
 
5.7%
t 169
 
5.7%
i 169
 
5.7%
Other values (8) 685
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2973
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 364
12.2%
e 337
11.3%
N 216
 
7.3%
m 216
 
7.3%
a 216
 
7.3%
l 216
 
7.3%
o 216
 
7.3%
h 169
 
5.7%
t 169
 
5.7%
i 169
 
5.7%
Other values (8) 685
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2973
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 364
12.2%
e 337
11.3%
N 216
 
7.3%
m 216
 
7.3%
a 216
 
7.3%
l 216
 
7.3%
o 216
 
7.3%
h 169
 
5.7%
t 169
 
5.7%
i 169
 
5.7%
Other values (8) 685
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2973
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 364
12.2%
e 337
11.3%
N 216
 
7.3%
m 216
 
7.3%
a 216
 
7.3%
l 216
 
7.3%
o 216
 
7.3%
h 169
 
5.7%
t 169
 
5.7%
i 169
 
5.7%
Other values (8) 685
23.0%

Blood Pressure
Categorical

Distinct25
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
130/85
99 
125/80
65 
140/95
65 
120/80
45 
115/75
32 
Other values (20)
68 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters2244
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.8%

Sample

1st row126/83
2nd row125/80
3rd row125/80
4th row140/90
5th row140/90

Common Values

ValueCountFrequency (%)
130/85 99
26.5%
125/80 65
17.4%
140/95 65
17.4%
120/80 45
12.0%
115/75 32
 
8.6%
135/90 27
 
7.2%
140/90 4
 
1.1%
125/82 4
 
1.1%
132/87 3
 
0.8%
128/85 3
 
0.8%
Other values (15) 27
 
7.2%

Length

2024-05-01T20:18:44.427560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
130/85 99
26.5%
140/95 65
17.4%
125/80 65
17.4%
120/80 45
12.0%
115/75 32
 
8.6%
135/90 27
 
7.2%
140/90 4
 
1.1%
125/82 4
 
1.1%
132/87 3
 
0.8%
128/85 3
 
0.8%
Other values (15) 27
 
7.2%

Most occurring characters

ValueCountFrequency (%)
1 420
18.7%
/ 374
16.7%
0 357
15.9%
5 333
14.8%
8 244
10.9%
3 139
 
6.2%
2 137
 
6.1%
9 107
 
4.8%
4 75
 
3.3%
7 49
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2244
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 420
18.7%
/ 374
16.7%
0 357
15.9%
5 333
14.8%
8 244
10.9%
3 139
 
6.2%
2 137
 
6.1%
9 107
 
4.8%
4 75
 
3.3%
7 49
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2244
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 420
18.7%
/ 374
16.7%
0 357
15.9%
5 333
14.8%
8 244
10.9%
3 139
 
6.2%
2 137
 
6.1%
9 107
 
4.8%
4 75
 
3.3%
7 49
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2244
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 420
18.7%
/ 374
16.7%
0 357
15.9%
5 333
14.8%
8 244
10.9%
3 139
 
6.2%
2 137
 
6.1%
9 107
 
4.8%
4 75
 
3.3%
7 49
 
2.2%

Heart Rate
Real number (ℝ)

Distinct19
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.165775
Minimum65
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-01T20:18:44.639601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile65
Q168
median70
Q372
95-th percentile78
Maximum86
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1356755
Coefficient of variation (CV)0.058941493
Kurtosis2.2864547
Mean70.165775
Median Absolute Deviation (MAD)2
Skewness1.2248235
Sum26242
Variance17.103812
MonotonicityNot monotonic
2024-05-01T20:18:44.686102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
68 94
25.1%
70 76
20.3%
72 69
18.4%
65 67
17.9%
75 36
 
9.6%
78 5
 
1.3%
85 3
 
0.8%
80 3
 
0.8%
84 2
 
0.5%
83 2
 
0.5%
Other values (9) 17
 
4.5%
ValueCountFrequency (%)
65 67
17.9%
67 2
 
0.5%
68 94
25.1%
69 2
 
0.5%
70 76
20.3%
72 69
18.4%
73 2
 
0.5%
74 2
 
0.5%
75 36
 
9.6%
76 2
 
0.5%
ValueCountFrequency (%)
86 2
 
0.5%
85 3
0.8%
84 2
 
0.5%
83 2
 
0.5%
82 1
 
0.3%
81 2
 
0.5%
80 3
0.8%
78 5
1.3%
77 2
 
0.5%
76 2
 
0.5%

Daily Steps
Real number (ℝ)

Distinct20
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6816.8449
Minimum3000
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-01T20:18:44.733004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3000
5-th percentile4930
Q15600
median7000
Q38000
95-th percentile10000
Maximum10000
Range7000
Interquartile range (IQR)2400

Descriptive statistics

Standard deviation1617.9157
Coefficient of variation (CV)0.23734084
Kurtosis-0.3940306
Mean6816.8449
Median Absolute Deviation (MAD)1000
Skewness0.17827733
Sum2549500
Variance2617651.1
MonotonicityNot monotonic
2024-05-01T20:18:44.786313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
8000 101
27.0%
6000 68
18.2%
5000 68
18.2%
7000 66
17.6%
10000 36
 
9.6%
5500 4
 
1.1%
3000 3
 
0.8%
3500 3
 
0.8%
4000 3
 
0.8%
6800 3
 
0.8%
Other values (10) 19
 
5.1%
ValueCountFrequency (%)
3000 3
 
0.8%
3300 2
 
0.5%
3500 3
 
0.8%
3700 2
 
0.5%
4000 3
 
0.8%
4100 2
 
0.5%
4200 2
 
0.5%
4800 2
 
0.5%
5000 68
18.2%
5200 2
 
0.5%
ValueCountFrequency (%)
10000 36
 
9.6%
8000 101
27.0%
7500 2
 
0.5%
7300 2
 
0.5%
7000 66
17.6%
6800 3
 
0.8%
6200 1
 
0.3%
6000 68
18.2%
5600 2
 
0.5%
5500 4
 
1.1%

Sleep Disorder
Categorical

MISSING 

Distinct2
Distinct (%)1.3%
Missing219
Missing (%)58.6%
Memory size3.1 KiB
Sleep Apnea
78 
Insomnia
77 

Length

Max length11
Median length11
Mean length9.5096774
Min length8

Characters and Unicode

Total characters1474
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSleep Apnea
2nd rowSleep Apnea
3rd rowInsomnia
4th rowInsomnia
5th rowSleep Apnea

Common Values

ValueCountFrequency (%)
Sleep Apnea 78
 
20.9%
Insomnia 77
 
20.6%
(Missing) 219
58.6%

Length

2024-05-01T20:18:44.843493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T20:18:44.890875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
sleep 78
33.5%
apnea 78
33.5%
insomnia 77
33.0%

Most occurring characters

ValueCountFrequency (%)
e 234
15.9%
n 232
15.7%
p 156
10.6%
a 155
10.5%
S 78
 
5.3%
l 78
 
5.3%
78
 
5.3%
A 78
 
5.3%
I 77
 
5.2%
s 77
 
5.2%
Other values (3) 231
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1474
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 234
15.9%
n 232
15.7%
p 156
10.6%
a 155
10.5%
S 78
 
5.3%
l 78
 
5.3%
78
 
5.3%
A 78
 
5.3%
I 77
 
5.2%
s 77
 
5.2%
Other values (3) 231
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1474
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 234
15.9%
n 232
15.7%
p 156
10.6%
a 155
10.5%
S 78
 
5.3%
l 78
 
5.3%
78
 
5.3%
A 78
 
5.3%
I 77
 
5.2%
s 77
 
5.2%
Other values (3) 231
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1474
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 234
15.9%
n 232
15.7%
p 156
10.6%
a 155
10.5%
S 78
 
5.3%
l 78
 
5.3%
78
 
5.3%
A 78
 
5.3%
I 77
 
5.2%
s 77
 
5.2%
Other values (3) 231
15.7%

Interactions

2024-05-01T20:18:42.944602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:40.305022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:40.656630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.031567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.472527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.809655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.162440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.492059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.990997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:40.351658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:40.700661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.080434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.517479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.856182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.205502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.535806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:43.033952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:40.394948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:40.776446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.122491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.560836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.902630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.247392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.577490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:43.078121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:40.439300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:40.819298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.164299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.601566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.947482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.287379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.617803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:43.121740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:40.482177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:40.859569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.204418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.643189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.988914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.327708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.657645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:43.168259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:40.527683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:40.903331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.247787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.685944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.031668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.370580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.824242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:43.211542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:40.569953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:40.946101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.289976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.725797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.074749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.409208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.862792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:43.254620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:40.611405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:40.986712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.427501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:41.765981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.114998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.448140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-01T20:18:42.901555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-01T20:18:43.320952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-01T20:18:43.410202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Person IDGenderAgeOccupationSleep DurationQuality of SleepPhysical Activity LevelStress LevelBMI CategoryBlood PressureHeart RateDaily StepsSleep Disorder
01Male27Software Engineer6.16426Overweight126/83774200NaN
12Male28Doctor6.26608Normal125/807510000NaN
23Male28Doctor6.26608Normal125/807510000NaN
34Male28Sales Representative5.94308Obese140/90853000Sleep Apnea
45Male28Sales Representative5.94308Obese140/90853000Sleep Apnea
56Male28Software Engineer5.94308Obese140/90853000Insomnia
67Male29Teacher6.36407Obese140/90823500Insomnia
78Male29Doctor7.87756Normal120/80708000NaN
89Male29Doctor7.87756Normal120/80708000NaN
910Male29Doctor7.87756Normal120/80708000NaN
Person IDGenderAgeOccupationSleep DurationQuality of SleepPhysical Activity LevelStress LevelBMI CategoryBlood PressureHeart RateDaily StepsSleep Disorder
364365Female59Nurse8.09753Overweight140/95687000Sleep Apnea
365366Female59Nurse8.09753Overweight140/95687000Sleep Apnea
366367Female59Nurse8.19753Overweight140/95687000Sleep Apnea
367368Female59Nurse8.09753Overweight140/95687000Sleep Apnea
368369Female59Nurse8.19753Overweight140/95687000Sleep Apnea
369370Female59Nurse8.19753Overweight140/95687000Sleep Apnea
370371Female59Nurse8.09753Overweight140/95687000Sleep Apnea
371372Female59Nurse8.19753Overweight140/95687000Sleep Apnea
372373Female59Nurse8.19753Overweight140/95687000Sleep Apnea
373374Female59Nurse8.19753Overweight140/95687000Sleep Apnea